On approximation by reproducing kernel spaces in weighted \(L^p\) spaces
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Publication:2425847
DOI10.1007/s11424-007-9061-yzbMath1147.68072OpenAlexW2163324156MaRDI QIDQ2425847
Publication date: 7 May 2008
Published in: Journal of Systems Science and Complexity (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11424-007-9061-y
Related Items (4)
Learning rates for the kernel regularized regression with a differentiable strongly convex loss ⋮ On the K-functional in learning theory ⋮ Convergence analysis for kernel-regularized online regression associated with an RRKHS ⋮ Error analysis of multicategory support vector machine classifiers
Cites Work
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- On the degree of approximation by spherical translations
- Some extremal properties of polynomials and inverse inequalities of approximation theory
- Averages and \(K\)-functionals related to the Laplacian
- Best approximation and \(K\)-functionals
- The covering number in learning theory
- Degree of approximation by neural and translation networks with a single hidden layer
- Approximation properties of zonal function networks using scattered data on the sphere
- Regularization networks and support vector machines
- On approximation by spherical zonal translation networks based on Bochner-Riesz means
- On the mathematical foundations of learning
- Capacity of reproducing kernel spaces in learning theory
- Radial Basis Functions
- ESTIMATING THE APPROXIMATION ERROR IN LEARNING THEORY
- Shannon sampling and function reconstruction from point values
- A Convolution Structure for Jacobi Series
- Theory of Reproducing Kernels
- Scattered Data Approximation
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